A rural new energy multi-agent power distribution network planning method based on game theory

Abstract

The present application relates to a kind of rural new energy multi-agent distribution network planning method based on game theory.Use to solve the multi-agent benefit balance optimization problem of rural area new energy power grid construction, to ensure that high proportion of new energy distribution network in rural area, high proportion of new energy does not affect the safe and stable operation of superior power grid as basic principle, according to the existence of a large amount of available new energy resources in rural area, a multi-agent distribution network planning method for increasing the efficiency of rural new energy utilization is proposed, a dynamic game model including load aggregator, distribution network operator and rural resident user benefit as the main body is established, the model ensures the safe and stable operation of rural power grid, to ensure the maximization of the interests of load aggregator, distribution network operator and rural resident user, complete rural area new energy planning solution.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rural power grid system planning and construction, and particularly relates to a rural new energy multi-agent power distribution network planning method based on game theory. BACKGROUND

[0002] Rural power grid is an important link to guarantee the power demand of the vast rural areas, and is crucial to driving rural consumption upgrading and accelerating the process of agricultural modernization. Therefore, the development of rural power grid should be placed in an important position.

[0003] With the continuous promotion of the construction of new power systems dominated by a high proportion of new energy, large-scale distributed renewable energy is connected to rural power grids. The development of comprehensive energy and the development of rural power grids are facing opportunities and challenges respectively. China is rich in renewable energy in rural areas, and the rural new energy system based on roof photovoltaic will become an important measure to promote rural economic development. Promoting the integration of high-proportion renewable energy and rural energy use is conducive to promoting the coordinated development of rural "source-grid-load-storage", but the traditional rural power grid lacks the ability to consume, and the connection of large-scale distributed power supply affects the power quality of the power grid, the automation of the distribution network and the action of the relay protection, and the harm of the power grid harmonic increases, and it is urgent to accelerate the construction of high-proportion renewable energy new rural power grid. SUMMARY

[0004] The present application aims to solve the problems in the background art, and provides a rural new energy multi-agent power distribution network planning method based on game theory, to ensure that the rural area high-proportion new energy power distribution network and the high-proportion new energy do not affect the safe and stable operation of the superior power grid as the fundamental principle, establish a benefit constraint model with load aggregator, power distribution network operator and rural resident user as the benefit subject, through the game relationship of the three benefit subjects, establish a multi-agent power distribution network complete information dynamic game planning model containing load aggregator, power distribution network operator and rural resident user, and realize the sustainable and efficient use of high-proportion new energy in rural areas.

[0005] To achieve the above purpose, the technical scheme of the present application is: a rural new energy multi-agent power distribution network planning method based on game theory, to ensure that the rural area high-proportion new energy power distribution network and the high-proportion new energy do not affect the safe and stable operation of the superior power grid as the fundamental principle, establish a benefit constraint model with load aggregator, power distribution network operator and rural resident user as the benefit subject, through the game relationship of the three benefit subjects, establish a multi-agent power distribution network complete information dynamic game planning model containing load aggregator, power distribution network operator and rural resident user, and realize the sustainable and efficient use of high-proportion new energy in rural areas.

[0006] In an embodiment of the present application, the power distribution network operator obtains profit by selling electricity to rural areas, and the main cost is the purchase of electricity from the upper grid, the maintenance cost of high proportion of new energy power supply and the purchase cost of high proportion of new energy power supply, which is expressed as:

[0007] Q1=H S -H T -H p

[0008]

[0009]

[0010] H p =H2·N

[0011] Wherein, Q1 represents the total profit obtained by the power distribution network operator per year by selling electricity to rural areas, H S represents the total income obtained by the power distribution network operator per year by selling electricity to rural areas, H T represents the purchase cost of the upper grid and the maintenance cost of high proportion of new energy power supply of the power distribution network operator per year, H p represents the purchase cost of high proportion of new energy power supply, NL1 represents the time of high proportion of new energy rural areas purchasing electricity from the upper grid, NL2 represents the time of high proportion of new energy rural areas supplying electricity, c t is the non-new energy electricity selling price, c t Sp is the new energy electricity selling price, c c is the purchase cost of high proportion of new energy rural areas purchasing electricity from the upper grid, W t is the amount of electricity purchased by high proportion of new energy rural areas from the upper grid, W t Sp is the supply amount of new energy electricity in rural areas, H1 is the unit new energy power supply maintenance cost, n is the average number of new energy power supply failures per year in rural areas, H2 is the unit new energy power supply purchase cost, and N is the total number of new energy power supplies in rural areas.

[0012] In an embodiment of the present application, the load aggregator obtains income by bargaining through changing flexible point quantity, and the income expression of the load aggregator is:

[0013]

[0014] Wherein, Q2 represents the income of the load aggregator, is the total income obtained by the flexible load of the load aggregator d at time t, which is L t Sp

[0015] ​In an embodiment of the present application, the electricity sales revenue of rural residents in a rural area with a high proportion of new energy is:

[0016]

[0017] wherein Q3 represents the electricity sales revenue of rural residents in a rural area with a high proportion of new energy, represents the electricity delivered by rural residents to the system.

[0018] In an embodiment of the present application, the constraint condition is:

[0019] (1) Distributed power supply capacity constraint of the distribution network

[0020]

[0021] wherein P PV,i is the distributed photovoltaic installation capacity at different positions in the distribution network, is the maximum capacity allowed to be installed for the distributed photovoltaic power station at different positions in the distribution network;

[0022] (2) Distributed power supply power flow constraint of the distribution network

[0023]

[0024] wherein P p,i,t is the main network power at time t, P E,i,t is the distributed photovoltaic output power at time t, P load,i,t is the load required power at time t, and P l,i,t is the network loss power at time t.

[0025] (3) Node voltage constraint of the distribution network

[0026] U EMIN,i ≤ U E,i ≤ U EMAX,i

[0027] wherein U E,i represents the voltage of any node in the distribution network system, U EMIN,i represents the minimum voltage allowed in the distribution network system, and U EMAX,i represents the maximum voltage allowed in the distribution network system.

[0028] Compared with the prior art, the present application has the following beneficial effects: the present application can ensure that the high-proportion new energy distribution network in rural areas and the high-proportion new energy do not affect the safe and stable operation of the superior power grid as a fundamental principle, establish a benefit constraint model taking the load aggregator, the distribution network operator and the rural resident user as the benefit subject, establish a multi-agent distribution network complete information dynamic game planning model including the load aggregator, the distribution network operator and the rural resident user through the game relationship of the three benefit subjects, and realize the sustainable and efficient use of the high-proportion new energy in rural areas. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 It is a model schematic diagram of the rural new energy multi-agent distribution network planning method based on the game theory. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0031] In the description of the present application, it should be noted that the terms "upper", "lower", "inner", "outer", "front end", "rear end", "two ends", "one end", "the other end" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0032] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "provided with", "connection" and the like should be understood broadly, for example, "connection" can be fixed connection, can also be detachable connection, or integral connection; can be mechanical connection, can also be electrical connection; can be directly connected, can also be indirectly connected through an intermediate medium, can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0033] The present application is based on the fact that there are a large number of available new energy resources in rural areas, and proposes a multi-agent distribution network planning method to increase the utilization efficiency of new energy in rural areas, establishes a dynamic game model including the interests of load aggregators, distribution network operators and rural resident users, and ensures the maximization of the interests of the three parties under the condition of safe and stable operation of the rural power grid, and completes the new energy planning solution in rural areas.

[0034] Please refer to Figure 1 The present application provides a multi-agent distribution network planning method based on game theory in rural areas, which is based on the principle of ensuring the safe and stable operation of the high-proportion new energy distribution network in rural areas and the high-proportion new energy, and establishes a benefit constraint model with load aggregators, distribution network operators and rural resident users as the main interests, and establishes a multi-agent distribution network complete information dynamic game planning model including load aggregators, distribution network operators and rural resident users through the game relationship of the three interest subjects, to realize the sustainable and efficient utilization of high-proportion new energy in rural areas.

[0035] The technical solution adopted by the present application to solve the technical problems is:

[0036] Further, the distribution network operator obtains profit by selling electric energy to rural areas, and its main costs are the purchase of electric energy from the superior power grid, the maintenance cost of high-proportion new energy power supply and the purchase cost of high-proportion new energy power supply, and its expression is:

[0037] Q1=H S -H T -H p

[0038]

[0039]

[0040] H p =H2·N

[0041] Wherein, Q1 represents the total profit obtained by the distribution network operator by selling electric energy to rural areas every year, H S represents the total income obtained by the distribution network operator by selling electric energy to rural areas every year, H T represents the purchase cost of the superior power grid and the maintenance cost of high-proportion new energy power supply of the distribution network operator every year, H p represents the purchase cost of high-proportion new energy power supply, NL1 represents the time of high-proportion new energy rural areas purchasing electric power from the superior power grid, NL2 represents the time of high-proportion new energy rural areas supplying power, c t is the non-new energy electric energy selling price, c tSp is the electricity price of new energy electricity, c c is the cost of purchasing electricity from the upper-level power grid in the rural area with a high proportion of new energy, W t is the amount of electricity purchased from the upper-level power grid in the rural area with a high proportion of new energy, W t Sp is the supply amount of new energy electricity in the rural area, H1 is the unit new energy source maintenance cost, n is the average number of new energy source failures in the rural area per year, H2 is the unit new energy source purchase cost, and N is the total number of new energy sources in the rural area.

[0042] Further, the load aggregator obtains revenue by negotiating by changing the flexible point amount, and the load aggregator revenue expression is:

[0043]

[0044] wherein Q2 represents the load aggregator revenue, is the flexible load electricity amount of the load aggregator d at time t, and L t Sp is the total revenue obtained at time t.

[0045] Further, the electricity sale income of the rural resident user in the rural area with a high proportion of new energy is:

[0046]

[0047] wherein Q3 represents the electricity sale income of the rural resident user in the rural area with a high proportion of new energy, represents the amount of electricity transmitted to the system by the rural resident.

[0048] Further, the constraint condition constraint is:

[0049] (1) Distributed power supply capacity constraint of distribution network

[0050]

[0051] wherein P PV,i is the installation capacity of different positions of distributed photovoltaic in the distribution network, is the maximum capacity allowed to be installed by the distributed photovoltaic power station at different positions in the distribution network;

[0052] (2) Distributed power supply power flow constraint of distribution network

[0053]

[0054] wherein P p,i,t is the main network power at time t, P E,i,t is the distributed photovoltaic output power at time t, P load,i,t is the load required power at time t, and Pl,i,t Ploss(t) is the network loss power at time t;

[0055] (3) Distribution network node voltage constraint

[0056] U EMIN,i ≤ U E,i ≤ U EMAX,i

[0057] wherein U E,i represents the voltage of any node in the distribution network system, U EMIN,i represents the minimum voltage allowed in the distribution network system, and U EMAX,i represents the maximum voltage allowed in the distribution network system.

[0058] It is apparent for a person skilled in the art that the present application is not limited to the details of the above exemplary embodiments, but can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application. Therefore, the present application should be considered in all respects as illustrative and not restrictive, the scope of the present application being defined by the appended claims rather than the above description, and it is intended to embrace all changes and modifications that fall within the meaning and scope of the equivalent elements of the claims. Any reference signs in the claims should not be construed as limiting the claims to the figures in which the reference signs are used, the present application being capable of a variety of modifications and changes for persons skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the present application.

Claims

1.A rural new energy multi-agent distribution network planning method based on game theory, characterized in that, A benefit constraint model is established with the load aggregator, distribution network operator and rural resident user as the benefit subject. Through the game relationship of the three benefit subjects, a multi-agent distribution network complete information dynamic game planning model is established, including the load aggregator, distribution network operator and rural resident user, to maximize the benefits of the three parties and complete the new energy planning solution in rural areas. The distribution network operator obtains profit by selling electricity to rural areas. Its main costs are the purchase of electricity from the upper grid, the maintenance cost of high proportion of new energy power supply and the purchase cost of high proportion of new energy power supply. Its expression is: Q1 = H S -H T -H p H p = H2· N wherein Q1 represents the total profit obtained by the power distribution network operator through the sale of electricity to rural areas each year, H S represents the total profit obtained by the power distribution network operator through the sale of electricity to rural areas each year, H T represents the cost of purchasing electricity from the upper-level power grid and the cost of maintaining high-proportion new energy power sources for the power distribution network operator each year, H p represents the cost of purchasing high-proportion new energy power sources, NL1 represents the time for the high-proportion new energy rural areas to purchase electricity from the upper-level power grid, and NL2 represents the time for the high-proportion new energy rural areas to supply new energy, is the electricity selling price of non-new energy, is the electricity selling price of new energy, c c is the cost of purchasing electricity from the upper-level power grid for the high-proportion new energy rural areas, is the amount of electricity purchased from the upper-level power grid for the high-proportion new energy rural areas, is the supply amount of new energy in rural areas, H1 is the unit maintenance cost of new energy sources, n is the average number of new energy source failures in rural areas each year, H2 is the unit purchase cost of new energy sources, and N is the total number of new energy sources in rural areas. The load aggregator obtains income by changing the flexible electricity consumption for bargaining. The income expression of the load aggregator is: wherein Q2 represents the load aggregator's revenue, is the flexible load power consumption of the load aggregator d at time t3, the total revenue obtained at time t3. The rural resident user's electricity selling income in rural areas with high proportion of new energy is: Q3 represents the rural resident user's electricity sales revenue in rural areas with a high proportion of new energy, represents the amount of electricity delivered by rural residents to the system; The constraint conditions are: (1) Distribution network distributed power capacity constraint wherein P PV,i is the distributed photovoltaic installation capacity at different locations within the distribution network, is the maximum capacity allowed for installation of distributed photovoltaic power plants at different locations within the distribution network; (2) Distribution network distributed power flow constraint Wherein, P p,i,t is the main grid power at time t, P E,i,t is the distributed photovoltaic output power at time t, P load,i,t is the load required power at time t, P l,i,t is the network loss power at time t; (3) Distribution network node voltage constraint U EMIN,i ≤U E,i ≤U EMAX,i where U E,i represents the voltage at any node in the distribution grid system, U EMIN,i represents the minimum allowable voltage in the distribution grid system, U EMAX,i represents the maximum allowable voltage in the distribution grid system.

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